A data warehouse is a database that is generally designed to support decision-making, querying, reporting and analysis. The data warehouse includes data that is populated from various data sources and servers associated with different data models. Examples of a data model include Enterprise Resource Planning solutions (ERP), Customer Relationship Management (CRM) solutions, and the like.
As the data warehouse is associated with various data sources it includes large amount of data and the data may be of different types and different formats. Thus, a large number of transactions is required by individuals to load the data from different sources to the data warehouse. To load the data in the data warehouse, the data should be first extracted form various sources and then it should be loaded into the data warehouse in a predefined format. Today, the process of loading data from the different sources to the data warehouse is automated using “Extract, Transform, and Load” (ETL) methodology or Extract, Load and Transform (ELT) methodology. The ETL methodology is used to correlate the data prior to loading the data into the data warehouse.
The ETL process first defines a data flow that includes data transformation activities for extracting data from different sources, for example flat files or relational tables. Thereafter, the process transforms the data; hence transformation refers to the processing of the data to put it into a more useful form or format acceptable by the data warehouse. The process then loads the data into a database, data warehouse, data mart, or staging table. Hence, loading in the ETL process refers to the loading of data into the tables of a relational database included in the data warehouse. However, in the ELT methodology, the data is first extracted, as it is done in the ETL methodology, and then the data is loaded in the database, data warehouse, data mart, or staging table. Thereafter, the transformation rules are applied on the loaded data. Therefore, in both methodologies, ETL and ELT, loading is an important aspect.
Today, different approaches may be used to store and load data into a database. According to one approach, a client application parses data and thereafter loads the data into databases. Generally, the client application generates Structured Query Language (SQL) commands, for example a SELECT command, an UPDATE command, an INSERT command, or the like, for loading and modifying the data. The commands are executed by a database server and enable the loading of data into corresponding columns of a database table in the database.
In another approach, data is stored directly into a database without going through a SQL engine. This is done using various database software modules, generally known as database stages. The database stages may include control files for loading data. A client application can determine the types of the data using the database stages. The client application then creates an array of data corresponding to columns of a table in a relational database. Thereafter, the client application can populate the array with the data that is to be loaded in the corresponding column. Further, the client application converts the array into a stream of data, which the database server can directly convert into the database's data blocks. In the above method, the ETL process has several database stages to handle loading of data into data warehouse. These employ native database provided tools to load data into the warehouse. Thus, a separate database stage is required for loading data into different databases.
Hence, there exists a need to efficiently manage loading of data into the databases.